Inter-annual changes and growth trends mapping of mangrove using Landsat time series imagery

Mangroves, as the most prolific but vulnerable ecosystems, necessitate continuous monitoring for effective conservation. However, continuous mangrove mapping is challenging due to extensive land use changes and highly intertidal dynamics. In this study, a novel mangrove index, named the Composite Mangrove Index (CMI), was developed to map and assess the growth trends of mangroves, based on the truth that the spectral-temporal features of mangrove wetland environment related to greenness, moisture, and bare soil. To facilitate long-term mapping of mangroves, the Continuous Change Detection and Classification (CCDC) algorithm was utilized on the Google Earth Engine platform (GEE). The innovative mangrove mapping framework using the CMI based CCDC was applied in three diverse regions (i.e. Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in southern China, Sundarbans in India and Bangladesh, and Gulf of Paria (GP) in northern Venezuela). The results showed that annual mangrove maps of these three typical regions from 2000 to 2020 achieved overall accuracy exceeding 92%, indicating the ability of CMI to capture the temporal inter-annual characteristics of mangroves. The area of mangroves represented a total increase of 700 ha in the GBA, while slightly decreasing in the Sundarbans and the GP. By comparing Fractional Vegetation Cover (FVC) and CMI with two mangrove types, CMI proved to be effective in not only assessing mangrove growth status but also monitoring recovery and degradation growth trends of mangroves, surpassing some existing indices (MI, EVI, TCA). Controlled experiments also demonstrated that CMI outperformed in mangrove classification compared to some mangrove indices (MI, MVI, CMRI). The distinctive CMI values can offer a rapid method to detect and quantify the growth trends for mangrove afforestation, restoration and even degradation. Therefore, the proposed CMI with CCDC provides new slight to monitor the ongoing efforts in mangrove conservation and shed light on understanding the mangrove dynamics.

Authors

Institutions

Publication Details

Journal
GIScience & Remote Sensing
Published
2025-03-24
DOI
https://doi.org/10.1080/15481603.2025.2480422
Citations
7
Primary Topic
Coastal wetland ecosystem dynamics
Type
article
Field-Weighted Citation Impact
12.01

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Inter-annual changes and growth trends mapping of mangrove using Landsat time series imagery

Xiaohui Huang, Yingchun Fu, Guoan Tang, Peifeng Ma et al.
7 citations
GIScience & Remote Sensing
Coastal wetland ecosystem dynamics
12.01
article

Inter-annual changes and growth trends mapping of mangrove using Landsat time series imagery

Xiaohui Huang, Yingchun Fu, Guoan Tang, Peifeng Ma, Shu‐Ting Wu, Ye Chen, Yufei Xue, Liangyun Liu, Hu Ding
article en
7 citations

Abstract

Mangroves, as the most prolific but vulnerable ecosystems, necessitate continuous monitoring for effective conservation. However, continuous mangrove mapping is challenging due to extensive land use changes and highly intertidal dynamics. In this study, a novel mangrove index, named the Composite Mangrove Index (CMI), was developed to map and assess the growth trends of mangroves, based on the truth that the spectral-temporal features of mangrove wetland environment related to greenness, moisture, and bare soil. To facilitate long-term mapping of mangroves, the Continuous Change Detection and Classification (CCDC) algorithm was utilized on the Google Earth Engine platform (GEE). The innovative mangrove mapping framework using the CMI based CCDC was applied in three diverse regions (i.e. Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in southern China, Sundarbans in India and Bangladesh, and Gulf of Paria (GP) in northern Venezuela). The results showed that annual mangrove maps of these three typical regions from 2000 to 2020 achieved overall accuracy exceeding 92%, indicating the ability of CMI to capture the temporal inter-annual characteristics of mangroves. The area of mangroves represented a total increase of 700 ha in the GBA, while slightly decreasing in the Sundarbans and the GP. By comparing Fractional Vegetation Cover (FVC) and CMI with two mangrove types, CMI proved to be effective in not only assessing mangrove growth status but also monitoring recovery and degradation growth trends of mangroves, surpassing some existing indices (MI, EVI, TCA). Controlled experiments also demonstrated that CMI outperformed in mangrove classification compared to some mangrove indices (MI, MVI, CMRI). The distinctive CMI values can offer a rapid method to detect and quantify the growth trends for mangrove afforestation, restoration and even degradation. Therefore, the proposed CMI with CCDC provides new slight to monitor the ongoing efforts in mangrove conservation and shed light on understanding the mangrove dynamics.

GIScience & Remote SensingVol. 62(1)
Ministry of Education of the People's Republic of China (CN), Nanjing Normal University (CN), Chinese University of Hong Kong (HK), South China Normal University (CN), Chinese Academy of Sciences (CN), Ministry of Natural Resources (CN), Aerospace Information Research Institute (CN), China Power Engineering Consulting Group (China) (CN)
National Natural Science Foundation of China, Humanities and Social Science Fund of Ministry of Education of China, Science and Technology Plan Projects of Tibet Autonomous Region, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 2%
Coastal wetland ecosystem dynamics
12.01
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.